fdth (version 1.1-8)

fdth-package: Frequency Distribution Tables, Histograms and Poligons

Description

The fdth package contains a set of functions which easily allows the user to make frequency distribution tables (fdt), its associated histograms and frequency poligons (absolut, relative and cumulative). The fdt can be formatted in many ways which may be suited to publication in many different ways (papers, books, etc). The plot method (S3) is the histogram which can be dealt with the easiness and flexibility of a high level function.

Details

The frequency of a particular observation is the number of times the observation occurs in the data. The distribution of a variable is the pattern of frequencies of the observation. Frequency distribution table fdt can be used for both ordinal and continuous variables. The R environment provides a set of functions (generally low level) enabling the user to perfom a fdt and the associated graphical representation, the histogram. A fdt plays an important role to summarize data information and is the basis for the estimation of probability density function used in parametrical inference. However, for novices or ocasional users of R, it can be laborious to find out all necessary funtions and graphical parameters to do a normatized and pretty fdt and the associated histogram ready for publications. That is the aim of this package, i.e, to allow the user to do (using a few, simple and flexible high level set of S3 functions) with ease and flexibility both: the fdt and histogram. The input data for univariated is generally a vector. For multivariated data can be used both: a data.frame, in this case also alowing grouping all numerical variables according to one categorical, or matrices. The simplest way to run fdt is done by supplying only the x object, for example: d <- fdt(x). In this case all necessary default values (breaks and right) ("Sturges" and FALSE respectivelly) will be used. It can be provided also:

• xandk(number of class intervals);
• x,start(left endpoint of the first class interval) andend(right endpoint of the last class interval); or
• x,start,endandh(class interval width).

These options make the fdt very easy and flexible. The fdt object stores information to be used by methods summary, print and plot. The result of plot is a histogram or poligon (absolut, relative or cummulative). The methods summary, print and plot provide a reasonable set of parameters to format and plot the fdt object in a pretty (and publishable) way.

See Also

hist provided by graphics; table, cut both provided by base and hist.data.frame provided by Hmisc package.

Examples

library (fdth)

#======================
# Vectors: univariated
#======================
set.seed(1)

x <- rnorm(n=1e3,
           mean=5,
           sd=1)

(d <- fdt(x))

# Histograms
plot(d)  # Absolut frequency histogram

plot(d,
     main='My title')

plot(d,
     x.round=3,
     col='darkgreen')

plot(d,
     x.las=2)

plot(d,
     x.round=3,
     x.las=2,
     xlab=NULL)

plot(d,
     v=TRUE,
     cex=.8,
     x.round=3,
     x.las=2,
     xlab=NULL,
     col=rainbow(11))

plot(d,
     type='fh')    # Absolut frequency histogram

plot(d,
     type='rfh')   # Relative frequency histogram

plot(d,
     type='rfph')  # Relative frequency (%) histogram

plot(d,
     type='cdh')   # Cumulative density histogram

plot(d,
     type='cfh')   # Cumulative frequency histogram

plot(d,
     type='cfph')  # Cumulative frequency (%) histogram

# Poligons
plot(d,
     type='fp')    # Absolut frequency poligon

plot(d,
     type='rfp')   # Relative frequency poligon

plot(d,
     type='rfpp')  # Relative frequency (%) poligon

plot(d,
     type='cdp')   # Cumulative density poligon

plot(d,
     type='cfp')   # Cumulative frequency poligon

plot(d,
     type='cfpp')  # Cumulative frequency (%) poligon

# Density
plot(d,
     type='d')     # Density

# Summary
d

summary(d)  # the same

print(d)    # the same

show(d)     # the same

summary(d,
        format=TRUE)      # It can not be what you want to publications!

summary(d,
        format=TRUE,
        pattern='%.2f')   # Huumm ..., good, but ... Can it be better?

summary(d,
        col=c(1:2, 4, 6),
        format=TRUE,
        pattern='%.2f')   # Yes, it can!

range(x)                  # To know x

summary(fdt(x,
            start=1, 
            end=9,
            h=1),
        col=c(1:2, 4, 6),
        format=TRUE,
        pattern='%d')     # Is it nice now?

# The fdt.object
d[['table']]                        # Stores the feq. dist. table (fdt)
d[['breaks']]                       # Stores the breaks of fdt
d[['breaks']]['start']              # Stores the left value of the first class
d[['breaks']]['end']                # Stores the right value of the last class
d[['breaks']]['h']                  # Stores the class interval
as.logical(d[['breaks']]['right'])  # Stores the right option

# Theoretical curve and fdt
x <- rnorm(1e5,
           mean=5, 
           sd=1)
plot(fdt(x,
         k=100),
     type='d',
     col=heat.colors(100))

curve(dnorm(x,
            mean=5, 
            sd=1),
      col='darkgreen',
      add=TRUE, 
      lwd=2)

#=============================================
# Data.frames: multivariated with categorical
#=============================================
mdf <- data.frame(X1=rep(LETTERS[1:4], 25),
                  X2=as.factor(rep(1:10, 10)),
                  Y1=c(NA, NA, rnorm(96, 10, 1), NA, NA),
                  Y2=rnorm(100, 60, 4),
                  Y3=rnorm(100, 50, 4),
                  Y4=rnorm(100, 40, 4))

(d <- fdt(mdf))

# Histograms
plot(d,
     v=TRUE)

plot(d,
     col='darkgreen')

plot(d,
     col=rainbow(8))

plot(d,
     type='fh')

plot(d,
     type='rfh')

plot(d,
     type='rfph')

plot(d, 
     type='cdh')

plot(d,
     type='cfh')

plot(d,
     type='cfph')

# Poligons
plot(d,
     v=TRUE,
     type='fp')

plot(d,
     type='rfp')

plot(d,
     type='rfpp')

plot(d,
     type='cdp')

plot(d,
     type='cfp')

plot(d,
     type='cfpp') 

# Density
plot(d,
     type='d') 

# Summary
d

summary(d)  # the same

print(d)    # the same

show(d)     # the same

summary(d,
        format=TRUE)

summary(d,
        format=TRUE, 
        pattern='%05.2f')  # regular expression

summary(d,
        col=c(1:2, 4, 6), 
        format=TRUE,
        pattern='%05.2f')

print(d,
      col=c(1:2, 4, 6))

print(d,
      col=c(1:2, 4, 6), 
      format=TRUE,
      pattern='%05.2f')

# Using by
levels(mdf$X1)

summary(fdt(mdf,
            k=5,
            by='X1'))

plot(fdt(mdf,
         k=5,
         by='X1'),
     col=rainbow(5))

levels(mdf$X2)

summary(fdt(mdf,
            breaks='FD',
            by='X2'), 
        round=3)

plot(fdt(mdf,
         breaks='FD',
         by='X2'))

summary(fdt(iris,
            k=5),
        format=TRUE,
        patter='%04.2f')

plot(fdt(iris,
         k=5),
     col=rainbow(5))

levels(iris$Species)

summary(fdt(iris,
            k=5,
            by='Species'),
        format=TRUE,
        patter='%04.2f')

plot(fdt(iris,
         k=5,
         by='Species'),
     v=TRUE)

#=========================
# Matrices: multivariated
#=========================
summary(fdt(state.x77),
        col=c(1:2, 4, 6),
        format=TRUE)

plot(fdt(state.x77))

# Very big
summary(fdt(volcano,
            right=TRUE),
        col=c(1:2, 4, 6),
        round=3,
        format=TRUE,
        pattern='%05.1f')

plot(fdt(volcano,
         right=TRUE))